Spatial transcriptomic characterization of pathologic niches in IPF

Despite advancements in antifibrotic therapy, idiopathic pulmonary fibrosis (IPF) remains a medical condition with unmet needs. Single-cell RNA sequencing (scRNA-seq) has enhanced our understanding of IPF but lacks the cellular tissue context and gene expression localization that spatial transcriptomics provides. To bridge this gap, we profiled IPF and control patient lung tissue using spatial transcriptomics, integrating the data with an IPF scRNA-seq atlas. We identified three disease-associated niches with unique cellular compositions and localizations. These include a fibrotic niche, consisting of myofibroblasts and aberrant basaloid cells, located around airways and adjacent to an airway macrophage niche in the lumen, containing SPP1+ macrophages. In addition, we identified an immune niche, characterized by distinct lymphoid cell foci in fibrotic tissue, surrounded by remodeled endothelial vessels. This spatial characterization of IPF niches will facilitate the identification of drug targets that disrupt disease-driving niches and aid in the development of disease relevant in vitro models.


Figure S1 :
Figure S1: Overview of tissue sections used for Visium for FFPE and CytAssist assays.(A) H&E-stained tissue sections of IPF and control patient lungs, used for the indicated assays of either 10x Genomics Visium for FFPE or 10x Genomics CytAssist for FFPE, or both.(B) The integrated spatial data of all tissue section is visualized as UMAP.The color code illustrates the disease status, the different technologies, and the donors.(C-D) The plots show the comparison of tissue annotated by a pathologist and the respective predicted cell type abundance from cell2location after spot deconvolution for (C) a control section and (D-E) two IPF sections.

Figure S2 :
Figure S2: Overview of PF-ILD scRNAseq atlas data and description of compartments.(A) The integrated scRNAseq data of the PF-ILD atlas is visualized through uniform manifold approximation and projection (UMAP).The color code illustrates the different studies/datasets, diagnosis, and their combination.(B) UMAPs represent the reduced atlas dataset to only IPF and control diagnosis.The color code illustrates the different studies/datasets, diagnosis, and their combination.(C-D) The indicated marker genes were used to select clusters for subsetting the data into compartments.(E-J) The compartments are represented with their respective UMAPs and top 2 marker genes per cell type within the compartment, for (E-F) EPCAM+ Epithelial cells, (G-H) COL1A1+ mesenchymal cells, (I-J) CLDN5+ endothelial cells.

Figure S3 :
Figure S3: Second part of Overview of scRNAseq atlas data and description of compartments.(A-D) The compartments are represented with their respective UMAPs and top 2 marker genes per cell type within the compartment, for (A-B) Lymphoid cells, (C-D) Myeloid cells.(E) The color code of the UMAP indicates all annotated cell types.(F) The heatmap shows normalized mean expression for accepted cell type marker genes per cell type.

Figure S4 :
Figure S4: Validation of cell2locatio predicted cell types.(A) Spatial plots exemplary show one control and IPF section with cell2location predicted fibroblast subtype cell type abundances.Desmin (DES) is shown as a marker for blood vessels and Keratin 5 (KRT5) as a marker for airways.(B) The cartoon illustrates on how the comparison of real single cells from Xenium data with predicted cell2location cell type abundances were performed, applying pseudo Visium spots on the Xenium data.(C) Heatmap displays the Pearson correlation of real cell types in pseudo Visium spots in the Xenium data versus cell2location predicted cell type abundances in those pseudo spots.(D) Heatmap shows the same correlation as in (C) but across samples.

Figure S5 :
Figure S5: Characterization of NMF inferred cellular niches.(A) The heatmap shows normalized cell type abundance for each of the 9 factors as calculated by the non-matrix factorization (NMF) of the cell2location package.Factor 8 only scored for 12 spots and was combined with Factor 0 into the SMCs_Adv_Meso niche.(B) The heatmap shows normalized expression of top10 genes per niche as result of the allmarkers() function from scanpy(55).(C) The bar graph illustrated relative frequencies of the niches in IPF or control sections.(D) The bar graphs illustrate the distribution of total cell type frequency across all niches (bluE), and the contribution of cell types to one niche as percentage of all cell types in one niche (orangE), for the three disease associated niches.(E) The heatmap shows the composition of all niches as percentages of cell types.(F) The heatmap shows the pathway activity score per spot, summarized per niche.The R package decoupleR(66) was used to score the pathway activity as provided by the R package PROGENy(66).(G) The heatmap shows the interactions between the niches as percentage per row, calculated by the function spatial_neighbors() in squidpy(61) for the direct neighbors (1 ring of spots) or the externed neighborhood (3 rings of spots).(H-I) Heatmaps show normalized neighbor interactions for each niche in (H) control and (I) IPF tissue.

Figure S6 :
Figure S6: Pathological cellular niches across all tissue sections.(A-C)The spatial plots display the distribution of the three in IPF emerging pathological cell type niches across all tissue sections, as well as the numeric NMF factor that was used for the assignment of spots to a niche, for (A) the fibrotic niche, (B) the immune niche and (C) the airway macrophage niche.

Figure S7 :
Figure S7: Validation of IPF associated niches in adjacent tissue sections of Visium samples.(A, E) H&E images of adjacent tissue sections of (A) IPF_patient_1 and (E) IPF_patient_2.(B-H) Xenium mRNA in situ hybridization data for marker genes of respective cell types in the (B, F) fibrotic niche, with co-localization of KRT5-KRT17+ aberrant basaloid cells with CTHRC1+ myofibroblasts next to KRT5+KRT17+ basal cells and MMP7+ airway cells, (C, G) airway macrophage niche with co-localization of SPP1+ macrophages inside the airway lumen, lined with FOXJ1+ ciliated cells, KRT17+ TB-SCs and MMP7+ airway cells and (D, H) immune niche with MS4A1 for B and plasma cells, CD3D for T cells and CD1C for dendritic cells within a distinct foci, surrounded by PLVAP for ectopic endothelial bronchial vessels.

Figure S8 :
Figure S8: Xenium validation of IPF associated niches in additional IPF patient tissue sections.(A, E, I) H&E images of additional tissue sections of (A) IPF_patient_4, (E) IPF_patient_5 and (I) IPF_patient_6.(B-L) Xenium mRNA in situ hybridization data for marker genes of respective cell types in the (B, F, J) fibrotic niche, with co-localization of KRT5-KRT17+ aberrant basaloid cells with CTHRC1+ myofibroblasts next to KRT5+KRT17+ basal cells and MMP7+ airway cells, (C, G, K) airway macrophage niche with co-localization of SPP1+ macrophages inside the airway lumen, lined with FOXJ1+ ciliated cells, KRT17+ TB-SCs and MMP7+ airway cells and (D, H, L) immune niche with MS4A1 for B and plasma cells, CD3D for T cells and CD1C for dendritic cells within a distinct foci, surrounded by PLVAP for ectopic endothelial bronchial vessels.

Figure S9 :
Figure S9: Protein validation of IPF associated niches on adjacent tissue sections of Visium samples.(A, C) COMET high-plex sequential protein immunofluorescence on adjacent tissue section showing the fibrotic niche with co-localization of KRT5-KRT17+MMP7+ aberrant basaloid cells with CTHRC1+ myofibroblasts next to KRT5+KRT17+ basal cells and MMP7+ airway cells and (B, D) COMET sequential protein immunofluorescence on adjacent tissue section showing the airway macrophage niche with airways lined with FOXJ1 for ciliated cells, KRT17 for TB-SC and MMP7 for airway cells and the airway lumen filled with CD68+ macrophages.

Figure S10 :
Figure S10: Localization of Fibrotic niche associated cell types.(A-C) The spatial plots display cell type abundance across all tissue sections for (A) Myofibroblasts, (B) Aberrant basaloid cells, and (C) TB-SCs.

Figure S11 :
Figure S11: Localization and characterization of fibrotic niche.(A-D) For two IPF tissue sections, the spatial plots display (A, C) the distribution of the fibrotic niche across the slides and zoom ins of two example regions with of selected cell type abundances, as well as (B, D) indicated marker gene expression.(E) The dotplot displays gene expression across cell types in the IPF single cell atlas.(F) Senescence gene score (CDKN1A, CDKN1B, CDKN2B, TP53, SERPINE1, GLB1) calculated using hotspot gene-module scoring(22), across all tissue sections.

Figure S12 :
Figure S12: Cell-cell signaling within the fibrotic niche.(A) Pie charts show the distribution of receptor-ligand pairs into the three cell communication categories and their associated signaling pathways.(B-C) Chord plot visualizing the interacting cell types for the ligand-receptor pair (B) EFNA5-EPHB2 and (C) EFNA5-EPHA3.(D) Gene expression of receptor and ligand genes across the niches and disease in the spatial Visium data.(E) Heatmap of significant ligand-receptor pairs from the secreted signaling category within the Fibrotic niche.(F-I) Chord plot visualizing the interacting cell types and heatmaps the expression of the involved genes in the spatial data for the ligand-receptor pairs (F, G) BMP5-(ACVR1+BMPR2) and (H, I) PDGFA-PDGFRB.

Figure S13 :
Figure S13: ECM-Receptor signaling within fibrotic niche.(A-C) Heatmaps of significant ligand-receptor pairs from the ECM-Receptor category within the airway fibrotic niche, split into major pathways (A) laminin pairs, (B) collagen pairs and (C) the remaining pairs.(D) Chord plot visualizing the interacting cell types for the ligand-receptor pair TNC-(ITGAV+ITGB6).